BinaryBuilder.jl
spack
BinaryBuilder.jl | spack | |
---|---|---|
5 | 52 | |
379 | 3,985 | |
1.1% | 1.6% | |
6.5 | 10.0 | |
9 days ago | 1 day ago | |
Julia | Python | |
GNU General Public License v3.0 or later | Apache-2.0 or MIT |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BinaryBuilder.jl
-
Is Julia suitable today as a scripting language?
There are some efforts and the startup times are getting better with every release and there's BinaryBuilder.jl.
-
Because cross-compiling binaries for Windows is easier than building natively
There is the Julia package https://github.com/JuliaPackaging/BinaryBuilder.jl which creates an environment that fakes being another, but with the correct compilers and SDKs . It's used to build all the binary dependencies
-
Discussion Thread
https://binarybuilder.org/. You can do it manually obviously, but this is easier.
-
PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
> The main pain point is probably the lack of standard, multi-environment packaging solutions for natively compiled code.
Are you talking about something like BinaryBuilder.jl[1], which provides native binaries as julia-callable wrappers?
--
[1] https://binarybuilder.org
-
What to do about GPU packages on PyPI?
Julia did that for binary dependencies for a few years, with adapters for several linux platforms, homebrew, and for cross-compiled RPMs for Windows. It worked, to a degree -- less well on Windows -- but the combinatorial complexity led to many hiccups and significant maintenance effort. Each Julia package had to account for the peculiarities of each dependency across a range of dependency versions and packaging practices (linkage policies, bundling policies, naming variations, distro versions) -- and this is easier in Julia than in (C)Python because shared libraries are accessed via locally-JIT'd FFI, so there is no need to eg compile extensions for 4 different CPython ABIs (Julia also has syntactic macros which can be helpful here).
To provide a better experience for both package authors and users, as well as reducing the maintenance burden, the community has developed and migrated to a unified system called BinaryBuilder (https://binarybuilder.org) over the past 2-3 years. BinaryBuilder allows targeting all supported platforms with a single build script and also "audits" build products for common compatibility and linkage snafus (similar to some of the conda-build tooling and auditwheel). There was a nice talk at AlpineConf recently (https://alpinelinux.org/conf/) covering some of this history and detailing BinaryBuilder, although I'm not sure how to link into the video.
All that to say: it can work to an extent, but it has been tried various times before. The fact that conda and manylinux don't use system packages was not borne out of inexperience, either. The idea of "make binaries a distro packager's problem" sounds like a simplifying step, but that doesn't necessarily work out.
spack
-
Autodafe: "freeing your freeing your project from the clammy grip of autotools."
> Are we talking about the same autotools?
Yes. Instead of figuring out how to do something particular with every single software package, I can do a --with-foo or --without-bar or --prefix=/opt/baz-1.2.3, and be fairly confident that it will work the way I want.
Certainly with package managers or (FreeBSD) Ports a lot is taken care of behind the scenes, but the above would also help the package/port maintainers as well. Lately I've been using Spack for special-needs compiles, but maintainer ease also helps there, but there are still cases one a 'fully manual' compile is still done.
> Suffice it to say, I prefer to work with handwritten makefiles.
Having everyone 'roll their own' system would probably be worse, because any "mysteriously failure" then has to be debugged specially for each project.
Have you tried Spack?
* https://spack.io
* https://spack.readthedocs.io/en/latest/
-
FreeBSD has a(nother) new C compiler: Intel oneAPI DPC++/C++
Well, good luck with that, cause it's broken.
Previous release miscompiled Python [1]
Current release miscompiles bison [2]
[1] https://github.com/spack/spack/issues/38724
[2] https://github.com/spack/spack/issues/37172#issuecomment-181...
-
Essential Command Line Tools for Developers
gh is available via Homebrew, MacPorts, Conda, Spack, Webi, and as a…
-
The Curious Case of MD5
> I can't count the number of times I've seen people say "md5 is fine for use case xyz" where in some counterintuitive way it wasn't fine.
I can count many more times that people told me that md5 was "broken" for file verification when, in fact, it never has been.
My main gripe with the article is that it portrays the entire legal profession as "backwards" and "deeply negligent" when they're not actually doing anything unsafe -- or even likely to be unsafe. And "tech" knows better. Much of tech, it would seem, has no idea about the use cases and why one might be safe or not. They just know something's "broken" -- so, clearly, we should update.
> Just use a safe one, even if you think you "don't need it".
Here's me switching 5,700 or so hashes from md5 to sha256 in 2019: https://github.com/spack/spack/pull/13185
Did I need it? No. Am I "compliant"? Yes.
Really, though, the main tangible benefit was that it saved me having to respond to questions and uninformed criticism from people unnecessarily worried about md5 checksums.
- Spack Package Manager v0.21.0
- Show HN: FlakeHub – Discover and publish Nix flakes
-
Nixhub: Search Historical Versions of Nix Packages
[1] https://github.com/spack/spack/blob/develop/var/spack/repos/...
-
Cython 3.0 Released
In Spack [1] we can express all these constraints for the dependency solver, and we also try to always re-cythonize sources. The latter is because bundled cythonized files are sometimes forward incompatible with Python, so it's better to just regenerate those with an up to date cython.
[1] https://github.com/spack/spack/
-
Linux server for physics simulations
You want to look at the tools used for HPC systems, these are generally very well tried and tested and can be setup for single machine usage. Remote access - we use ssh, but web interfaces such as Open On Demand exist - https://openondemand.org/. For managing Jobs, Slurm is currently the most popular option - https://slurm.schedmd.com/documentation.html. For a module system (to load software and libraries per user), Spack is a great - https://spack.io/. You might also want to consider containerisation options, https://apptainer.org/ is a good option.
-
Simplest way to get latest gcc for any platform ?
git clone https://github.com/spack/spack.git ./spack/bin/spack install gcc
What are some alternatives?
functorch - functorch is JAX-like composable function transforms for PyTorch.
HomeBrew - 🍺 The missing package manager for macOS (or Linux)
Yggdrasil - Collection of builder repositories for BinaryBuilder.jl
nixpkgs - Nix Packages collection & NixOS
HTTP.jl - HTTP for Julia
nix-processmgmt - Experimental Nix-based process management framework
dh-virtualenv - Python virtualenvs in Debian packages
Ansible - Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.
RDKit - The official sources for the RDKit library
ohpc - OpenHPC Integration, Packaging, and Test Repo
StarWarsArrays.jl - Arrays indexed as the order of Star Wars movies
NixOS-docker - DEPRECATED! Dockerfiles to package Nix in a minimal docker container